pybind11/README.md

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![pybind11 logo](https://github.com/pybind/pybind11/raw/master/docs/pybind11-logo.png)
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# pybind11 — Seamless operability between C++11 and Python
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**pybind11** is a lightweight header-only library that exposes C++ types in Python
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and vice versa, mainly to create Python bindings of existing C++ code. Its
goals and syntax are similar to the excellent
[Boost.Python](http://www.boost.org/doc/libs/1_58_0/libs/python/doc/) library
by David Abrahams: to minimize boilerplate code in traditional extension
modules by inferring type information using compile-time introspection.
The main issue with Boost.Python—and the reason for creating such a similar
project—is Boost. Boost is an enormously large and complex suite of utility
libraries that works with almost every C++ compiler in existence. This
compatibility has its cost: arcane template tricks and workarounds are
necessary to support the oldest and buggiest of compiler specimens. Now that
C++11-compatible compilers are widely available, this heavy machinery has
become an excessively large and unnecessary dependency.
Think of this library as a tiny self-contained version of Boost.Python with
everything stripped away that isn't relevant for binding generation. Without
comments, the core header files only require ~4K lines of code and depend on
Python (2.7 or 3.x, or PyPy2.7 >= 5.7) and the C++ standard library. This
compact implementation was possible thanks to some of the new C++11 language
features (specifically: tuples, lambda functions and variadic templates). Since
its creation, this library has grown beyond Boost.Python in many ways, leading
to dramatically simpler binding code in many common situations.
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Tutorial and reference documentation is provided at
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[http://pybind11.readthedocs.org/en/master](http://pybind11.readthedocs.org/en/master).
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A PDF version of the manual is available
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[here](https://media.readthedocs.org/pdf/pybind11/master/pybind11.pdf).
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## Core features
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pybind11 can map the following core C++ features to Python
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- Functions accepting and returning custom data structures per value, reference, or pointer
- Instance methods and static methods
- Overloaded functions
- Instance attributes and static attributes
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- Arbitrary exception types
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- Enumerations
- Callbacks
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- Iterators and ranges
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- Custom operators
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- Single and multiple inheritance
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- STL data structures
- Iterators and ranges
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- Smart pointers with reference counting like ``std::shared_ptr``
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- Internal references with correct reference counting
- C++ classes with virtual (and pure virtual) methods can be extended in Python
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## Goodies
In addition to the core functionality, pybind11 provides some extra goodies:
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- Python 2.7, 3.x, and PyPy (PyPy2.7 >= 5.7) are supported with an
implementation-agnostic interface.
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- It is possible to bind C++11 lambda functions with captured variables. The
lambda capture data is stored inside the resulting Python function object.
- pybind11 uses C++11 move constructors and move assignment operators whenever
possible to efficiently transfer custom data types.
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- It's easy to expose the internal storage of custom data types through
Pythons' buffer protocols. This is handy e.g. for fast conversion between
C++ matrix classes like Eigen and NumPy without expensive copy operations.
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- pybind11 can automatically vectorize functions so that they are transparently
applied to all entries of one or more NumPy array arguments.
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- Python's slice-based access and assignment operations can be supported with
just a few lines of code.
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- Everything is contained in just a few header files; there is no need to link
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against any additional libraries.
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- Binaries are generally smaller by a factor of at least 2 compared to
equivalent bindings generated by Boost.Python. A recent pybind11 conversion
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of PyRosetta, an enormous Boost.Python binding project,
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[reported](http://graylab.jhu.edu/RosettaCon2016/PyRosetta-4.pdf) a binary
size reduction of **5.4x** and compile time reduction by **5.8x**.
- When supported by the compiler, two new C++14 features (relaxed constexpr and
return value deduction) are used to precompute function signatures at compile
time, leading to smaller binaries.
- With little extra effort, C++ types can be pickled and unpickled similar to
regular Python objects.
## Supported compilers
1. Clang/LLVM 3.3 or newer (for Apple Xcode's clang, this is 5.0.0 or newer)
2. GCC 4.8 or newer
3. Microsoft Visual Studio 2015 Update 3 or newer
4. Intel C++ compiler 17 or newer (16 with pybind11 v2.0 and 15 with pybind11 v2.0 and a [workaround](https://github.com/pybind/pybind11/issues/276))
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5. Cygwin/GCC (tested on 2.5.1)
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## About
This project was created by [Wenzel Jakob](http://rgl.epfl.ch/people/wjakob).
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Significant features and/or improvements to the code were contributed by
Jonas Adler,
Sylvain Corlay,
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Trent Houliston,
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Axel Huebl,
@hulucc,
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Sergey Lyskov
Johan Mabille,
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Tomasz Miąsko,
Dean Moldovan,
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Ben Pritchard,
Jason Rhinelander,
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Boris Schäling,
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Pim Schellart,
Ivan Smirnov, and
Patrick Stewart.
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### License
pybind11 is provided under a BSD-style license that can be found in the
``LICENSE`` file. By using, distributing, or contributing to this project,
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you agree to the terms and conditions of this license.